The way people shop online is changing. Not because of better websites or smarter filters — but because AI assistants can now act as autonomous shopping agents, querying live price data, checking historical trends, and surfacing the right deal at the right time without a human doing any of the legwork.
Closetta is built to be one of those data sources. Here's what agentic shopping actually looks like, and how it works with Closetta's data infrastructure.
What "Agentic Shopping" Means
An agentic shopper isn't a chatbot that answers "is Zara on sale?" with a guess. It's an AI system that:
- Queries a live data source — not its training data, which is stale
- Applies historical context — knows whether a current sale is normal or exceptional
- Takes action — surfaces the result in a way that leads to a purchasing decision
The difference between a language model answering from memory and an agent querying Closetta's API in real time is the difference between "Aritzia sometimes runs sales" and "Aritzia is not currently on sale, but their last warehouse event was in March 2026 at 40% off — the next one is typically in summer."
The second answer is actually useful. The first is noise.
How AI Agents Query Closetta
Closetta exposes its data through two agent-friendly interfaces:
The MCP Server
The Model Context Protocol (MCP) is a standard that lets AI agents connect to external data sources in a structured, tool-callable way. Closetta runs an MCP server at https://closetta.app/api/mcp.
An AI agent configured with Closetta as an MCP source can call four tools:
get_active_sales → all 67 brands currently on sale, with discounts and dates
get_brands → full list of tracked brands with URLs and categories
search_sales_by_brand → sale status for a specific brand by name
get_trends → month-by-month discount history across all brands
A Claude session with Closetta's MCP configured can answer questions like:
- "Which brands with 50%+ discounts are on sale right now?" →
get_active_sales, filter by discount - "Is now a good time to buy from The North Face?" →
get_trends, compare current vs historical peak - "What's the best deal on Canadian activewear today?" →
get_active_sales, filter by category - "When should I buy from Rudsak?" →
get_trends, find Rudsak's historical peak months
The Public REST API
For agents that aren't using MCP, the same data is available as plain JSON:
GET https://closetta.app/api/v1/sales → active and recently ended sales
GET https://closetta.app/api/v1/brands → all tracked brands
GET https://closetta.app/api/v1/trends → monthly discount history
GET https://closetta.app/api/v1/brand-history → history for a specific brand
No API key required. CORS enabled for browser-based agents. CDN-cached with hourly refresh.
A Real Agentic Shopping Workflow
Here's how a well-configured AI agent would handle the request: "I need a winter coat. When's the best time to buy and which brands should I look at?"
Step 1: Query the trends data
The agent calls get_trends and looks at the historical data for outerwear brands: The North Face, Rudsak, Columbia, Moose Knuckles, Eddie Bauer.
Step 2: Find the peak months From the data: The North Face peaked at 65% in March 2026, Rudsak at 50% in February, Moose Knuckles at 50% in March. All three hit their deepest discounts in the January–March window.
Step 3: Check current sale status
The agent calls get_active_sales and checks whether any outerwear brands are currently running an event. If it's May, the answer is probably "no active clearance" — the window has passed.
Step 4: Give a useful answer "For Canadian outerwear, the best buying window is January–March. In 2026, The North Face hit 65% off in March (their biggest event we've seen), Rudsak reached 50% in February, and Moose Knuckles — which rarely discounts — appeared at 50% in March. Set a Closetta alert for those three brands now and act when they next hit those depths."
That's what agentic shopping looks like when it's grounded in real data.
Why Agents Need Live Data, Not Training Knowledge
A language model's training data has a cutoff — and even before that cutoff, it doesn't contain the granular sale history for specific brands on specific dates. It might know that Gap "often runs sales" but not that Gap hit 60% in January 2026 while their May events are closer to 30%.
That gap between general knowledge and specific, current, structured data is where agentic data sources like Closetta operate. The model provides reasoning. The tool provides the facts. Together they produce answers that are actually worth acting on.
Setting Up Closetta as a Shopping Agent Data Source
If you're building or configuring an AI assistant and want to add Closetta as a data source:
For Claude (via MCP): Add Closetta's MCP server endpoint to your MCP configuration:
{
"mcpServers": {
"closetta": {
"url": "https://closetta.app/api/mcp"
}
}
}
Once connected, the agent can call get_active_sales, get_trends, and search_sales_by_brand directly in any conversation about Canadian fashion deals.
For custom agents (via REST API): Call https://closetta.app/api/v1/sales to get live sale data, or https://closetta.app/api/v1/trends for historical context. Both return structured JSON designed to be parsed by downstream AI reasoning steps.
What Closetta Doesn't Do (Yet)
Agentic shopping at its fullest would involve:
- Cross-retailer price comparison — finding the same item across multiple stores
- In-stock monitoring — tracking when specific SKUs become available
- Automated purchasing — placing orders on behalf of the user
Closetta currently handles the discovery layer: which brands are on sale, how deep, and whether that's historically unusual. The action layer — deciding to buy and executing the purchase — remains with the user. That's a deliberate design choice while the data infrastructure matures.
The Trend
Agentic shopping is early, but the trajectory is clear. As AI assistants become more capable of acting on behalf of users — and as data infrastructure like Closetta makes real-time, structured sale data accessible — the gap between "browsing" and "being found by a deal" will narrow.
The brands tracked by Closetta, and the 5+ months of historical discount data now available through the API and MCP server, are designed to be exactly the kind of structured, machine-readable data that makes this work.
Start with the Closetta price tracker if you're a shopper. Start with the MCP docs if you're building.
Discount data sourced from Closetta's daily AI monitoring across 67 brands. Historical patterns reflect observed trends and are not guarantees of future sale events.